diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,2 +1,23 @@
 # redback-data-warehouse
 Data Warehouse storage of code and configurations
+
+## Garmin Run Data – ETL Pipeline Update
+
+This ETL pipeline processes `Garmin_run_data.csv` and includes:
+
+### Data cleaning:
+- Removes duplicate rows
+- Standardizes column names (lowercase, underscores)
+- Converts timestamps to datetime
+- Fills missing numeric values with column means
+- Removes outliers in `heart_rate` (keeps values between 30–220 bpm)
+- Converts distance from meters to kilometers
+- Converts speed from m/s to km/h
+
+### Data aggregation:
+- Groups data by year and week
+- Calculates total runs, total distance (km), average speed (km/h), and average pace (min/km) per week
+
+### Outputs:
+- `cleaned_garmin_run_data.csv` → cleaned dataset
+
diff --git a/Requirement Gathering (4).pdf b/Requirement Gathering (4).pdf
new file mode 100644
index 0000000..aaa2831
Binary files /dev/null and b/Requirement Gathering (4).pdf differ
diff --git a/etl_scripts/ETL pipeline.ipynb b/etl_scripts/ETL pipeline.ipynb
new file mode 100644
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+++ b/etl_scripts/ETL pipeline.ipynb	
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "27156c5d-4dfb-4f95-a4e6-d88976d9c7c5",
+   "metadata": {},
+   "source": [
+    "# Importing required libraries\r\n",
+    "# We use pandas for data manipulation and matplotlib for visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ced94ba9-4fef-457e-9c36-855cc56c90bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0501979d-4574-4efc-8409-43de021a8bb6",
+   "metadata": {},
+   "source": [
+    "# Extract – Load the Garmin running data\r\n",
+    "# Reading the original raw CSV file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5445f384-8e1a-4d1a-9936-49f6ae3cbacb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_raw = pd.read_csv(\"Garmin_run_data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6221e8e8-af73-4fbe-be56-d992d7f63132",
+   "metadata": {},
+   "source": [
+    "# Transform – Cleaning and enhancing the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "368a5cac-e8e5-444b-8dbe-9d96a7581f3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Cleaned data and weekly stats saved.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# ============================\n",
+    "# 📥 Load raw data\n",
+    "# ============================\n",
+    "df_raw = pd.read_csv(\"Garmin_run_data.csv\")\n",
+    "\n",
+    "# ============================\n",
+    "# 🧹 Data Cleaning\n",
+    "# ============================\n",
+    "\n",
+    "# 1. Remove duplicate rows\n",
+    "df_cleaned = df_raw.drop_duplicates()\n",
+    "\n",
+    "# 2. Standardize column names (lowercase, underscores)\n",
+    "df_cleaned.columns = [col.strip().lower().replace(\" \", \"_\") for col in df_cleaned.columns]\n",
+    "\n",
+    "# 3. Convert timestamps to datetime\n",
+    "if 'timestamp' in df_cleaned.columns:\n",
+    "    df_cleaned['timestamp'] = pd.to_datetime(df_cleaned['timestamp'], errors='coerce')\n",
+    "\n",
+    "# 4. Fill missing numeric values with column means\n",
+    "numeric_cols = df_cleaned.select_dtypes(include='number').columns\n",
+    "df_cleaned[numeric_cols] = df_cleaned[numeric_cols].fillna(df_cleaned[numeric_cols].mean())\n",
+    "\n",
+    "# 5. Remove outliers in heart_rate (keep values between 30 and 220 bpm)\n",
+    "if 'heart_rate' in df_cleaned.columns:\n",
+    "    df_cleaned = df_cleaned[(df_cleaned['heart_rate'] >= 30) & (df_cleaned['heart_rate'] <= 220)]\n",
+    "\n",
+    "# 6. Unit conversion: meters to kilometers\n",
+    "if 'distance' in df_cleaned.columns:\n",
+    "    df_cleaned['distance_km'] = df_cleaned['distance'] / 1000\n",
+    "\n",
+    "# 7. Unit conversion: speed from m/s to km/h\n",
+    "if 'speed' in df_cleaned.columns:\n",
+    "    df_cleaned['speed_kmh'] = df_cleaned['speed'] * 3.6\n",
+    "\n",
+    "# ============================\n",
+    "# 📊 Data Aggregation (Weekly Stats)\n",
+    "# ============================\n",
+    "\n",
+    "if 'timestamp' in df_cleaned.columns:\n",
+    "    # Extract week, month, year for grouping\n",
+    "    df_cleaned['week'] = df_cleaned['timestamp'].dt.isocalendar().week\n",
+    "    df_cleaned['month'] = df_cleaned['timestamp'].dt.month\n",
+    "    df_cleaned['year'] = df_cleaned['timestamp'].dt.year\n",
+    "\n",
+    "    # Group by year + week to compute stats\n",
+    "    weekly_stats = df_cleaned.groupby(['year', 'week']).agg(\n",
+    "        total_runs=('timestamp', 'count'),\n",
+    "        total_distance_km=('distance_km', 'sum'),\n",
+    "        average_speed_kmh=('speed_kmh', 'mean')\n",
+    "    ).reset_index()\n",
+    "\n",
+    "    # Calculate average pace (min/km) if speed exists\n",
+    "    if 'average_speed_kmh' in weekly_stats.columns:\n",
+    "        weekly_stats['average_pace_min_per_km'] = 60 / weekly_stats['average_speed_kmh']\n",
+    "\n",
+    "# ============================\n",
+    "# 💾 Save outputs\n",
+    "# ============================\n",
+    "\n",
+    "# Save cleaned data\n",
+    "df_cleaned.to_csv(\"cleaned_garmin_run_data.csv\", index=False)\n",
+    "\n",
+    "# Save weekly statistics (if generated)\n",
+    "if 'weekly_stats' in locals():\n",
+    "    weekly_stats.to_csv(\"weekly_stats_garmin_run_data.csv\", index=False)\n",
+    "\n",
+    "print(\"✅ Cleaned data and weekly stats saved.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1eb98d8e-3551-4b80-962a-810059c40992",
+   "metadata": {},
+   "source": [
+    "# Visualize – Ploting distributions for insight"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6425c501-52f4-44eb-b1a3-46c5ecec29dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0NNR07tzZzJo164q1u7RuHh4epnr16qZZs2ZmyJAh9ic3lJTn+fPnzV//+ldz6623murVqxt/f39zyy23mPHjx5tz587Z1zt58qR56KGHzPXXX29sNpu99gXbmzp16hVjGfN/P7ctW7aY++67z1x33XUmMDDQ/PnPfzbHjx93WD87O9uMHTvWREREGH9/f9OhQwezbdu2Qk9LMObi0wKio6ONp6enQ8zLn5ZgzMUnHjz//PMmKirKeHt7m7CwMPPUU0+ZU6dOOYyLiooyPXr0KLRflx8TxZk7d67x9PQ0R48eLbR+kyZNHObt3LnThIaGmpYtW5rff//dGFP0kzCKq3dxx+XLL79satWqZc6fP3/FfAFcmc2YUnxNFwAACzp//rwiIyM1ZsyYMt0a4ip5eXmqV6+e+vXrp9dee63c4wNWxD23AIBrlp+fnyZMmKBp06a55X7aK1m4cKHOnj2rv/3tb+UeG7Aq7rkFAFzThgwZoj/++EO//vqrmjVrVq6x8/Pz9f777zv1tA8AReO2BAAAAFgGtyUAAADAMmhuAQAAYBncc6uL9zwdPXpUgYGBZX6DDwAAANzP/P/nhIeHh8vDo/jrszS3ko4ePaqIiIiKTgMAAABXcPjw4RJf3lOhze26des0depUbdmyRceOHdNnn32mXr162ZcbYzRhwgTNmTNHp06dsr+qs0mTJvYx2dnZeu6557R48WJlZWWpS5cuevvtt8v0xqKCV2cePnxY1atXd9n+FScnJ0crV65UbGysvL293R7PKqib86idc6ibc6ibc6ib86idc6pa3TIyMhQREXHFV55XaHN77tw5NW/eXI8//rgefPDBQsunTJmiadOmaf78+WrQoIFeffVVxcTEaM+ePfYdGzVqlP7zn//ogw8+UFBQkMaMGaOePXtqy5Yt8vT0LFUeBbciVK9evdya24CAAFWvXr1KHEyVBXVzHrVzDnVzDnVzDnVzHrVzTlWt25VuIa3Q5rZbt27q1q1bkcuMMZo+fbpeeukl+7vTFyxYoJCQEC1atEhDhw7V6dOnNXfuXP3P//yP/b30CxcuVEREhFatWqWuXbuW274AAACg4lXae27379+v1NRUxcbG2uf5+vqqQ4cOSkpK0tChQ7Vlyxbl5OQ4jAkPD1fTpk2VlJRUbHObnZ2t7Oxs+3RGRoaki7/B5OTkuGmP/k9BjPKIZSXUzXnUzjnUzTnUzTnUzXnUzjlVrW6lzbPSNrepqamSpJCQEIf5ISEhOnjwoH2Mj4+PbrjhhkJjCtYvysSJEzVhwoRC81euXKmAgICrTb3UEhMTyy2WlVA351E751A351A351A351E751SVumVmZpZqXKVtbgtcfl+FMeaK91pcacy4ceM0evRo+3TBDcqxsbHlds9tYmKiYmJiqtQ9LhWNujmP2jmHujmHujmHujmP2jmnqtWt4C/tV1Jpm9vQ0FBJF6/OhoWF2eenpaXZr+aGhobqwoULOnXqlMPV27S0NLVt27bYbfv6+srX17fQfG9v73L94ZZ3PKugbs6jds6hbs6hbs6hbs6jds6pKnUrbY6V9g1l0dHRCg0NdbhUfuHCBa1du9beuLZq1Ure3t4OY44dO6adO3eW2NwCAADAmir0yu3Zs2e1b98++/T+/fu1bds21axZU5GRkRo1apQSEhJUv3591a9fXwkJCQoICFC/fv0kSTVq1NCgQYM0ZswYBQUFqWbNmnruuefUrFkz+9MTAAAAcO2o0OZ28+bN6tSpk3264D7YAQMGaP78+Ro7dqyysrI0bNgw+0scVq5c6fDw3jfeeENeXl565JFH7C9xmD9/fqmfcQsAAADrqNDmtmPHjjLGFLvcZrMpPj5e8fHxxY7x8/PTjBkzNGPGDDdkCAAAgKqk0t5zCwAAAJRVpX1aAqq2Q4cOKT093aXbzM/PlyQlJyfLw6Pw72W1atVSZGSkS2MCAICqheYWLnfo0CE1bNRQWZlZLt2uv7+/Fi9erPbt2ysrq/C2/QP89VPKTzS4AABcw2hu4XLp6enKysxS3Ow4hTQIufIKpeRpPKUMacTSEcqz5TksO/7zcS0culDp6ek0twAAXMNobuE2IQ1CFNE8wmXbs+XapCSpdtPaMl7FfxERAABcu/hCGQAAACyD5hYAAACWQXMLAAAAy6C5BQAAgGXQ3AIAAMAyaG4BAABgGTS3AAAAsAyaWwAAAFgGzS0AAAAsg+YWAAAAlkFzCwAAAMuguQUAAIBl0NwCAADAMmhuAQAAYBk0twAAALAMmlsAAABYBs0tAAAALIPmFgAAAJZBcwsAAADLoLkFAACAZdDcAgAAwDJobgEAAGAZNLcAAACwDJpbAAAAWAbNLQAAACyD5hYAAACWQXMLAAAAy6C5BQAAgGXQ3AIAAMAyaG4BAABgGTS3AAAAsAyaWwAAAFgGzS0AAAAsg+YWAAAAlkFzCwAAAMuguQUAAIBl0NwCAADAMmhuAQAAYBk0twAAALAMmlsAAABYBs0tAAAALIPmFgAAAJZBcwsAAADLoLkFAACAZdDcAgAAwDIqdXObm5ur//qv/1J0dLT8/f1188036+9//7vy8/PtY4wxio+PV3h4uPz9/dWxY0ft2rWrArMGAABARanUze3kyZM1a9YszZw5UykpKZoyZYqmTp2qGTNm2MdMmTJF06ZN08yZM7Vp0yaFhoYqJiZGZ86cqcDMAQAAUBEqdXO7YcMGPfDAA+rRo4fq1Kmjhx56SLGxsdq8ebOki1dtp0+frpdeekl9+vRR06ZNtWDBAmVmZmrRokUVnD0AAADKm1dFJ1CSu+66S7NmzdLPP/+sBg0aKDk5Wd9++62mT58uSdq/f79SU1MVGxtrX8fX11cdOnRQUlKShg4dWuR2s7OzlZ2dbZ/OyMiQJOXk5CgnJ8d9O/T/FcQoj1gVIT8/X/7+/vI0nrLl2ly23YJtFbVNT+Mpf39/5efnW7auV8Pqx5y7UDfnUDfnUDfnUTvnVLW6lTZPmzHGuDkXpxlj9OKLL2ry5Mny9PRUXl6eXnvtNY0bN06SlJSUpHbt2unIkSMKDw+3rzdkyBAdPHhQK1asKHK78fHxmjBhQqH5ixYtUkBAgHt2BgAAAE7LzMxUv379dPr0aVWvXr3YcZX6yu2HH36ohQsXatGiRWrSpIm2bdumUaNGKTw8XAMGDLCPs9kcr+QZYwrNu9S4ceM0evRo+3RGRoYiIiIUGxtbYrFcJScnR4mJiYqJiZG3t7fb45W35ORktW/fXiOWjlDtprVdtl1brk2RP0Tq0B2HZLwcfyc7svOIZvSYoXXr1ql58+Yui2kVVj/m3IW6OYe6OYe6OY/aOaeq1a3gL+1XUqmb27/97W964YUX9Oijj0qSmjVrpoMHD2rixIkaMGCAQkNDJUmpqakKCwuzr5eWlqaQkJBit+vr6ytfX99C8729vcv1h1ve8cqLh4eHsrKylGfLK9SEuoLxMoW2m2fLU1ZWljw8PCxZU1ex6jHnbtTNOdTNOdTNedTOOVWlbqXNsVJ/oSwzM1MeHo4penp62h8FFh0drdDQUCUmJtqXX7hwQWvXrlXbtm3LNVcAAABUvEp95fa+++7Ta6+9psjISDVp0kRbt27VtGnT9MQTT0i6eDvCqFGjlJCQoPr166t+/fpKSEhQQECA+vXrV8HZAwAAoLxV6uZ2xowZevnllzVs2DClpaUpPDxcQ4cO1SuvvGIfM3bsWGVlZWnYsGE6deqU7rzzTq1cuVKBgYEVmDkAAAAqQqVubgMDAzV9+nT7o7+KYrPZFB8fr/j4+HLLCwAAAJVTpb7nFgAAACiLSn3lFqjMDh06pPT09HKLV6tWLUVGRpZbPAAAqiKaW8AJhw4dUsNGDZWVmVVuMf0D/PVTyk80uAAAlIDmFnBCenq6sjKzFDc7TiENin+msqsc//m4Fg5dqPT0dJpbAABKQHMLXIWQBiGKaB5R0WkAAID/jy+UAQAAwDJobgEAAGAZNLcAAACwDJpbAAAAWAbNLQAAACyDpyVUoOTkZHl4lM/vF7wAAAAAXAtobivAb7/9Jklq3769srLK5yUAvAAAAABcC2huK8CJEyckSX3/2VdB9YPcHo8XAAAAgGsFzW0FCq4XrPDm4RWdBgAAgGXwhTIAAABYBs0tAAAALIPmFgAAAJZBcwsAAADLoLkFAACAZdDcAgAAwDJobgEAAGAZNLcAAACwDJpbAAAAWAbNLQAAACyD5hYAAACWQXMLAAAAy6C5BQAAgGXQ3AIAAMAyaG4BAABgGTS3AAAAsAyaWwAAAFgGzS0AAAAsg+YWAAAAlkFzCwAAAMuguQUAAIBl0NwCAADAMmhuAQAAYBk0twAAALAMmlsAAABYBs0tAAAALMOrohNA+UlJSbFUHAAAgMvR3F4DMo5nyOZhU1xcXEWnAgAA4FY0t9eArNNZMvlGcbPjFNIgxO3xdq/arWWvLXN7HAAAgMvR3F5DQhqEKKJ5hNvjHP/5uNtjAAAAFIUvlAEAAMAyaG4BAABgGTS3AAAAsAyaWwAAAFiGU83t/v37XZ0HAAAAcNWcam7r1aunTp06aeHChTp//ryrc3Jw5MgRxcXFKSgoSAEBAbrtttu0ZcsW+3JjjOLj4xUeHi5/f3917NhRu3btcmtOAAAAqJycam6Tk5PVokULjRkzRqGhoRo6dKh++OEHV+emU6dOqV27dvL29tayZcu0e/duvf7667r++uvtY6ZMmaJp06Zp5syZ2rRpk0JDQxUTE6MzZ864PB8AAABUbk41t02bNtW0adN05MgRzZs3T6mpqbrrrrvUpEkTTZs2Tb///rtLkps8ebIiIiI0b9483XHHHapTp466dOmiunXrSrp41Xb69Ol66aWX1KdPHzVt2lQLFixQZmamFi1a5JIcAAAAUHVc1UscvLy81Lt3b3Xv3l1vv/22xo0bp+eee07jxo1T3759NXnyZIWFhTm9/S+//FJdu3bVww8/rLVr16p27doaNmyYBg8eLOnivb+pqamKjY21r+Pr66sOHTooKSlJQ4cOLXK72dnZys7Otk9nZGRIknJycpSTk+N0vqWVn58vSfI0nrLl2twez8vmJX9//yofr2BbRW3T03jK399f+fn55fYzLM+aFuxfSkqK/fgpi4J1tm7dKg+P0v1OGxQUpJtuuqnMsayk4Fgqj2PKCn777TedOHHCqePNGVY7RjnenEftnFPV6lbaPG3GGONskM2bN+vf//63PvjgA1WrVk0DBgzQoEGDdPToUb3yyis6c+bMVd2u4OfnJ0kaPXq0Hn74Yf3www8aNWqUZs+erf79+yspKUnt2rXTkSNHFB4ebl9vyJAhOnjwoFasWFHkduPj4zVhwoRC8xctWqSAgACn8wUAAIB7ZGZmql+/fjp9+rSqV69e7Dinmttp06Zp3rx52rNnj7p3764nn3xS3bt3d/gNfd++fWrYsKFyc3Od2wNJPj4+at26tZKSkuzznnnmGW3atEkbNmywN7dHjx51uEI8ePBgHT58WMuXLy9yu0VduY2IiFB6enqJxXKVrVu36tixY/o+8HuFNgt1f7zPt+rDkR9qxNIRqt20dpWNZ8u1KfKHSB2645CMl+Nhe2TnEc3oMUPr1q1T8+bNXRazOMnJyWrfvn2517TvP/squF5wmdf3NJ6688yd+j7we+XZ8q44Pm1fmj4c+WG51bOyysnJUWJiomJiYuTt7V3R6VRqBf8m+v6zr8LqhpXpeHOGFY9RjjfnUTvnVLW6ZWRkqFatWldsbp26LeGdd97RE088occff1yhoUU3Z5GRkZo7d64zm7cLCwtT48aNHeY1atRIn376qSTZY6empjo0t2lpaQoJCSl2u76+vvL19S0039vbu1x+uAW/BOTZ8go1ae6Qa3KVlZVlmXjGyxTabp4tT1lZWfLw8Ci3n2FF1DSofpDCm4dfeYXL2HJtUpIU2iy0VPmWdz0ru/I6N1RlBf8mguoHKbRJaJmON2dY+RjleHMetXNOValbaXN0qrndu3fvFcf4+PhowIABzmzerl27dtqzZ4/DvJ9//llRUVGSpOjoaIWGhioxMVEtWrSQJF24cEFr167V5MmTryo2AAAAqh6n7vSfN2+ePv7440LzP/74Yy1YsOCqkyrw7LPPauPGjUpISNC+ffu0aNEizZkzR08//bQkyWazadSoUUpISNBnn32mnTt3auDAgQoICFC/fv1clgcAAACqBqeu3E6aNEmzZs0qND84OFhDhgy56iu2BW6//XZ99tlnGjdunP7+978rOjpa06dP11/+8hf7mLFjxyorK0vDhg3TqVOndOedd2rlypUKDAx0SQ6oWlJSUiwVBwAAlI1Tze3BgwcVHR1daH5UVJQOHTp01UldqmfPnurZs2exy202m+Lj4xUfH+/SuKhaMo5nyOZhU1xcXEWnAgAAKpBTzW1wcLC2b9+uOnXqOMxPTk5WUFCQK/ICyiTrdJZMvlHc7DiFNCj+y4SusnvVbi17bZnb4wAAgLJxqrl99NFH9cwzzygwMFDt27eXJK1du1YjR47Uo48+6tIEgbIIaRCiiOYRbo9z/Ofjbo8BAADKzqnm9tVXX9XBgwfVpUsXeXld3ER+fr769++vhIQElyYIAAAAlJZTza2Pj48+/PBD/eMf/1BycrL8/f3VrFkz+yO6AAAAgIrgVHNboEGDBmrQoIGrcgEAAACuilPNbV5enubPn6+vv/5aaWlpys/Pd1i+evVqlyQHAAAAlIVTze3IkSM1f/589ejRQ02bNpXNZnN1XgAAAECZOdXcfvDBB/roo4/UvXt3V+cDAAAAOM2p1+/6+PioXr16rs4FAAAAuCpONbdjxozRP//5TxljXJ0PAAAA4DSnbkv49ttvtWbNGi1btkxNmjSRt7e3w/IlS5a4JDkAAACgLJxqbq+//nr17t3b1bkAAAAAV8Wp5nbevHmuzgMAAAC4ak7dcytJubm5WrVqlWbPnq0zZ85Iko4ePaqzZ8+6LDkAAACgLJy6cnvw4EHde++9OnTokLKzsxUTE6PAwEBNmTJF58+f16xZs1ydJwAAAHBFTl25HTlypFq3bq1Tp07J39/fPr937976+uuvXZYcAAAAUBZOPy3hu+++k4+Pj8P8qKgoHTlyxCWJAQAAAGXl1JXb/Px85eXlFZr/22+/KTAw8KqTAgAAAJzhVHMbExOj6dOn26dtNpvOnj2r8ePH80peAAAAVBinbkt444031KlTJzVu3Fjnz59Xv379tHfvXtWqVUuLFy92dY4AAABAqTjV3IaHh2vbtm1avHixfvzxR+Xn52vQoEH6y1/+4vAFMwAAAKA8OdXcSpK/v7+eeOIJPfHEE67MBwAAAHCaU83te++9V+Ly/v37O5UMAAAAcDWcam5HjhzpMJ2Tk6PMzEz5+PgoICCA5hYAAAAVwqmnJZw6dcrhc/bsWe3Zs0d33XUXXygDAABAhXGquS1K/fr1NWnSpEJXdQEAAIDy4rLmVpI8PT119OhRV24SAAAAKDWn7rn98ssvHaaNMTp27Jhmzpypdu3auSQxAAAAoKycam579erlMG2z2XTjjTeqc+fOev31112RFwAAAFBmTjW3+fn5rs4DAAAAuGouvecWAAAAqEhOXbkdPXp0qcdOmzbNmRAAAABAmTnV3G7dulU//vijcnNzdcstt0iSfv75Z3l6eqply5b2cTabzTVZAgAAAKXgVHN73333KTAwUAsWLNANN9wg6eKLHR5//HHdfffdGjNmjEuTBAAAAErDqXtuX3/9dU2cONHe2ErSDTfcoFdffZWnJQAAAKDCONXcZmRk6Pjx44Xmp6Wl6cyZM1edFAAAAOAMp5rb3r176/HHH9cnn3yi3377Tb/99ps++eQTDRo0SH369HF1jgAAAECpOHXP7axZs/Tcc88pLi5OOTk5Fzfk5aVBgwZp6tSpLk0QAAAAKC2nmtuAgAC9/fbbmjp1qn755RcZY1SvXj1Vq1bN1fkBAAAApXZVL3E4duyYjh07pgYNGqhatWoyxrgqLwAAAKDMnGpuT5w4oS5duqhBgwbq3r27jh07Jkl68skneQwYAAAAKoxTze2zzz4rb29vHTp0SAEBAfb5ffv21fLly12WHAAAAFAWTt1zu3LlSq1YsUI33XSTw/z69evr4MGDLkkMAAAAKCunrtyeO3fO4YptgfT0dPn6+l51UgAAAIAznGpu27dvr/fee88+bbPZlJ+fr6lTp6pTp04uSw4AAAAoC6duS5g6dao6duyozZs368KFCxo7dqx27dqlkydP6rvvvnN1jgAAAECpOHXltnHjxtq+fbvuuOMOxcTE6Ny5c+rTp4+2bt2qunXrujpHAAAAoFTKfOU2JydHsbGxmj17tiZMmOCOnAAAAACnlPnKrbe3t3bu3CmbzeaOfAAAAACnOXVbQv/+/TV37lxX5wIAAABcFae+UHbhwgW9++67SkxMVOvWrVWtWjWH5dOmTXNJcpebOHGiXnzxRY0cOVLTp0+XJBljNGHCBM2ZM0enTp3SnXfeqbfeektNmjRxSw7AtSQlJaXcYtWqVUuRkZHlFg8AYE1lam5//fVX1alTRzt37lTLli0lST///LPDGHfdrrBp0ybNmTNHt956q8P8KVOmaNq0aZo/f74aNGigV199VTExMdqzZ48CAwPdkgtgdRnHM2TzsCkuLq7cYvoH+OunlJ9ocAEAV6VMzW39+vV17NgxrVmzRtLF1+2++eabCgkJcUtyBc6ePau//OUv+te//qVXX33VPt8Yo+nTp+ull15Snz59JEkLFixQSEiIFi1apKFDh7o1L8Cqsk5nyeQbxc2OU0gD9/77lqTjPx/XwqELlZ6eTnMLALgqZWpujTEO08uWLdO5c+dcmlBRnn76afXo0UP33HOPQ3O7f/9+paamKjY21j7P19dXHTp0UFJSUrHNbXZ2trKzs+3TGRkZki4+CSInJ8dNe/F/8vPzJUmexlO2XPd/Mc/L5iV/f/8qH69gW0Vt0yr76K54JdWupHjh9cNVu0ntMscrK0/jKX9/f+Xn55fLv8HSKsilMuVUWeXn5xc6Rt35b6OyHjNXg+PNedTOOVWtbqXN02Yu71hL4OHhodTUVAUHB0uSAgMDlZycrJtvvtm5LEvhgw8+0GuvvaZNmzbJz89PHTt21G233abp06crKSlJ7dq105EjRxQeHm5fZ8iQITp48KBWrFhR5Dbj4+OLfIzZokWLinytMAAAACpWZmam+vXrp9OnT6t69erFjivTlVubzVbonlp3PhLs8OHDGjlypFauXCk/P78S87qUMabEvMaNG6fRo0fbpzMyMhQREaHY2NgSi+UqW7du1bFjx/R94PcKbRbq/nifb9WHIz/UiKUjVLup+6/CuSueLdemyB8ideiOQzJejr+TWWUf3RWvpNq5I15ZHdl5RDN6zNC6devUvHlzt8crrZycHCUmJiomJkbe3t4VnU6llpycrPbt22vE0hG6qeFNZTrenFFZj5mrwfHmPGrnnKpWt4K/tF9JmW9LGDhwoHx9fSVJ58+f11//+tdCT0tYsmRJWTZbrC1btigtLU2tWrWyz8vLy9O6des0c+ZM7dmzR5KUmpqqsLAw+5i0tLQS7wP29fW178OlvL29y+WH6+Fx8QlsebY8t534L5VrcpWVlWWZeMbLFNqu1fbRXfGKqp0745VWni1PWVlZ8vDwqJQn2PI6N1RlHh4ehY6Z0h5vzqjsx8zV4HhzHrVzTlWpW2lzLFNzO2DAAIdpd3+TukuXLtqxY4fDvMcff1wNGzbU888/r5tvvlmhoaFKTExUixYtJF18TNnatWs1efJkt+YGAACAyqdMze28efPclUeRAgMD1bRpU4d51apVU1BQkH3+qFGjlJCQoPr166t+/fpKSEhQQECA+vXrV665AgAAoOI59RKHymTs2LHKysrSsGHD7C9xWLlyJc+4BQAAuAZVueb2m2++cZi22WyKj49XfHx8heQDAACAysOjohMAAAAAXIXmFgAAAJZBcwsAAADLoLkFAACAZdDcAgAAwDJobgEAAGAZNLcAAACwDJpbAAAAWAbNLQAAACyD5hYAAACWQXMLAAAAy6C5BQAAgGXQ3AIAAMAyvCo6AQAokJKSUq7xatWqpcjIyHKNCQBwL5pbABUu43iGbB42xcXFlWtc/wB//ZTyEw0uAFgIzS2ACpd1Oksm3yhudpxCGoSUS8zjPx/XwqELlZ6eTnMLABZCcwug0ghpEKKI5hEVnQYAoArjC2UAAACwDJpbAAAAWAbNLQAAACyD5hYAAACWQXMLAAAAy+BpCQCuaSW9OCI/P1+SlJycLA+Pq78WwEsjAMD9aG4BXJNK8+IIf39/LV68WO3bt1dWVtZVx+SlEQDgfjS3AK5JpXlxhKfxlDKkEUtHKM+Wd1XxeGkEAJQPmlsA17SSXhxhy7VJSVLtprVlvEw5ZwYAcAZfKAMAAIBl0NwCAADAMmhuAQAAYBk0twAAALAMmlsAAABYBs0tAAAALIPmFgAAAJZBcwsAAADLoLkFAACAZdDcAgAAwDJobgEAAGAZNLcAAACwDJpbAAAAWAbNLQAAACyD5hYAAACWQXMLAAAAy6C5BQAAgGV4VXQCAHAtSUlJKbdYtWrVUmRkZLnFA4DKgOYWAMpBxvEM2TxsiouLK7eY/gH++inlJxpcANcUmlsAKAdZp7Nk8o3iZscppEGI2+Md//m4Fg5dqPT0dJpbANcUmlsAKEchDUIU0TyiotMAAMviC2UAAACwDJpbAAAAWEalbm4nTpyo22+/XYGBgQoODlavXr20Z88ehzHGGMXHxys8PFz+/v7q2LGjdu3aVUEZAwAAoCJV6uZ27dq1evrpp7Vx40YlJiYqNzdXsbGxOnfunH3MlClTNG3aNM2cOVObNm1SaGioYmJidObMmQrMHAAAABWhUn+hbPny5Q7T8+bNU3BwsLZs2aL27dvLGKPp06frpZdeUp8+fSRJCxYsUEhIiBYtWqShQ4dWRNoAAACoIJW6ub3c6dOnJUk1a9aUJO3fv1+pqamKjY21j/H19VWHDh2UlJRUbHObnZ2t7Oxs+3RGRoYkKScnRzk5Oe5K3y4/P1+S5Gk8Zcu1uT2el81L/v7+VT5ewbaK2qZV9tFd8UqqnTvilVV5xyttzLLW7WrjuZKn8ZS/v7/y8/PL7bx2+f65cz8L9i8lJcV+TnW3oKAg3XTTTW7bfsHPqTx+XlZD7ZxT1epW2jxtxhjj5lxcwhijBx54QKdOndL69eslSUlJSWrXrp2OHDmi8PBw+9ghQ4bo4MGDWrFiRZHbio+P14QJEwrNX7RokQICAtyzAwAAAHBaZmam+vXrp9OnT6t69erFjqsyV26HDx+u7du369tvvy20zGZzvDpgjCk071Ljxo3T6NGj7dMZGRmKiIhQbGxsicVyla1bt+rYsWP6PvB7hTYLdX+8z7fqw5EfasTSEardtHaVjWfLtSnyh0gduuOQjJfj72RW2Ud3xSupdu6IV1blHa+0Mctat6uN50pHdh7RjB4ztG7dOjVv3tzt8ZKTk9W+fXuNWDpCNzW8yWV1K05BPfv+s6+C6wW7Jcal0val6cORH7q1njk5OUpMTFRMTIy8vb3dEsOqqJ1zqlrdCv7SfiVVorkdMWKEvvzyS61bt87hT0KhoRcbw9TUVIWFhdnnp6WlKSSk+DcA+fr6ytfXt9B8b2/vcvnhenhc/B5fni3PbSf+S+WaXGVlZVkmnvEyhbZrtX10V7yiaufOeKVV3vHKGrO0dXNVPFfIs+UpKytLHh4e5XZeu3z/XFG34hTUM6h+kMKbh195hatUnvUsr/8WWRG1c05VqVtpc6zUT0swxmj48OFasmSJVq9erejoaIfl0dHRCg0NVWJion3ehQsXtHbtWrVt27a80wUAAEAFq9RXbp9++mktWrRIX3zxhQIDA5WamipJqlGjhvz9/WWz2TRq1CglJCSofv36ql+/vhISEhQQEKB+/fpVcPYAAAAob5W6uX3nnXckSR07dnSYP2/ePA0cOFCSNHbsWGVlZWnYsGE6deqU7rzzTq1cuVKBgYHlnC0AAAAqWqVubkvzIAebzab4+HjFx8e7PyEAAABUapX6nlsAAACgLGhuAQAAYBk0twAAALAMmlsAAABYBs0tAAAALIPmFgAAAJZBcwsAAADLoLkFAACAZdDcAgAAwDJobgEAAGAZlfr1uwCAq5OSkmKpOABwJTS3AGBBGcczZPOwKS4urqJTAYByRXMLABaUdTpLJt8obnacQhqEuD3e7lW7tey1ZW6PAwBXQnMLABYW0iBEEc0j3B7n+M/H3R4DAEqDL5QBAADAMmhuAQAAYBk0twAAALAMmlsAAABYBs0tAAAALIPmFgAAAJZBcwsAAADLoLkFAACAZdDcAgAAwDJobgEAAGAZNLcAAACwDJpbAAAAWAbNLQAAACyD5hYAAACWQXMLAAAAy6C5BQAAgGXQ3AIAAMAyaG4BAABgGV4VnQAAAFVFSkqK27adn58vSUpOTpaHx8VrT7Vq1VJkZKTbYsK9Dh06pPT09HKNyTFDcwsAwBVlHM+QzcOmuLg4t8Xw9/fX4sWL1b59e2VlZV2cF+Cvn1J+uuablaro0KFDatioobIys8o1LscMzS0AAFeUdTpLJt8obnacQhqEuCWGp/GUMqQRS0coz5an4z8f18KhC5Wenn5NNypVVXp6urIys9x6zFyOY+YimlsAAEoppEGIIppHuGXbtlyblCTVblpbxsu4Jca1rjxvEyi4hcWdxwyKRnMLAAAsr6JuE0D5o7kFAACWV963CexetVvLXlvm9jgojOYWAABUCFfdJlDUkyYuV963CRz/+bjbYxSntE/1KE3drqQyPp2B5hYAAJQ7V94mUNSTJq5FZX2qhyvqVhmfzkBzCwAAyp0rbxO4/EkTRbkWbhMo61M9SlO3klTWpzPQ3AIAUIm588URl8vOzpavr2+5xHLlbQKledJERd4mUN5KW1OrPqGD5hYAgEqoPF4ccTmbh00m3zpNDq5NNLcAAFRC5fHiiEsV/NmepwmgqqO5BQCgEivvb/dfC08TgLU599wHAAAAoBKiuQUAAIBl0NwCAADAMmhuAQAAYBk0twAAALAMyzS3b7/9tqKjo+Xn56dWrVpp/fr1FZ0SAAAAypklmtsPP/xQo0aN0ksvvaStW7fq7rvvVrdu3XTo0KGKTg0AAADlyBLN7bRp0zRo0CA9+eSTatSokaZPn66IiAi98847FZ0aAAAAylGVf4nDhQsXtGXLFr3wwgsO82NjY5WUlFTkOtnZ2crOzrZPnz59WpJ08uRJ5eTkuC/Z/y8jI0OZmZlKPZSq7HPZV17hKp345YT8/Px0bPsx5Z7NrbLxPI2nbsy8UQc3HlSeLa9cYhanqsUrqXbuiFdW5R2vtDHLWrerjedKFRnPnDEuq1tp4lmlnpcfb1bcR3fFK82/1aq8f+6KebXnuN9//V1+fn7KyMjQiRMnnEm5TM6cOSNJMuYKr4g2VdyRI0eMJPPdd985zH/ttddMgwYNilxn/PjxRhIfPnz48OHDhw+fKvY5fPhwib1hlb9yW8BmszlMG2MKzSswbtw4jR492j6dn5+vkydPKigoqNh1XCkjI0MRERE6fPiwqlev7vZ4VkHdnEftnEPdnEPdnEPdnEftnFPV6maM0ZkzZxQeHl7iuCrf3NaqVUuenp5KTU11mJ+WlqaQkJAi1/H19ZWvr6/DvOuvv95dKRarevXqVeJgqmyom/OonXOom3Oom3Oom/OonXOqUt1q1KhxxTFV/gtlPj4+atWqlRITEx3mJyYmqm3bthWUFQAAACpClb9yK0mjR4/WY489ptatW6tNmzaaM2eODh06pL/+9a8VnRoAAADKkSWa2759++rEiRP6+9//rmPHjqlp06b66quvFBUVVdGpFcnX11fjx48vdGsESkbdnEftnEPdnEPdnEPdnEftnGPVutmMudLzFAAAAICqocrfcwsAAAAUoLkFAACAZdDcAgAAwDJobgEAAGAZNLel8Pbbbys6Olp+fn5q1aqV1q9fX+L4tWvXqlWrVvLz89PNN9+sWbNmFRrz6aefqnHjxvL19VXjxo312WeflTmuMUbx8fEKDw+Xv7+/OnbsqF27dl3dzrpQRdRt4sSJuv322xUYGKjg4GD16tVLe/bscRgzcOBA2Ww2h8+f/vSnq99hF6mIusXHxxeqSWhoqMMYjrfCdatTp06hutlsNj399NP2MZX9eJNcX7tdu3bpwQcftNdn+vTpTsW91o650tSNc5xzdeMc51zdquw5rsSX88J88MEHxtvb2/zrX/8yu3fvNiNHjjTVqlUzBw8eLHL8r7/+agICAszIkSPN7t27zb/+9S/j7e1tPvnkE/uYpKQk4+npaRISEkxKSopJSEgwXl5eZuPGjWWKO2nSJBMYGGg+/fRTs2PHDtO3b18TFhZmMjIy3FeQUqqounXt2tXMmzfP7Ny502zbts306NHDREZGmrNnz9rHDBgwwNx7773m2LFj9s+JEyfcV4wyqKi6jR8/3jRp0sShJmlpaQ6xON4K1y0tLc2hZomJiUaSWbNmjX1MZT7ejHFP7X744Qfz3HPPmcWLF5vQ0FDzxhtvOBX3WjvmSlM3znHO1Y1znHN1q6rnOJrbK7jjjjvMX//6V4d5DRs2NC+88EKR48eOHWsaNmzoMG/o0KHmT3/6k336kUceMffee6/DmK5du5pHH3201HHz8/NNaGiomTRpkn35+fPnTY0aNcysWbPKsIfuUVF1u1xaWpqRZNauXWufN2DAAPPAAw+UdlfKVUXVbfz48aZ58+bF5sXxdtGVjreRI0eaunXrmvz8fPu8yny8GeOe2l0qKiqqyP9oco5zrm6X4xznqLi6cY5zzfFWVc5x3JZQggsXLmjLli2KjY11mB8bG6ukpKQi19mwYUOh8V27dtXmzZuVk5NT4piCbZYm7v79+5WamuowxtfXVx06dCg2t/JSUXUryunTpyVJNWvWdJj/zTffKDg4WA0aNNDgwYOVlpZWup1zo4qu2969exUeHq7o6Gg9+uij+vXXX+3LON7+b0xx27xw4YIWLlyoJ554QjabzWFZZTzeJPfVzhVxr8Vjzhmc40qPc9zVHW9V6RxHc1uC9PR05eXlKSQkxGF+SEiIUlNTi1wnNTW1yPG5ublKT08vcUzBNksTt+B/y5Jbeamoul3OGKPRo0frrrvuUtOmTe3zu3Xrpvfff1+rV6/W66+/rk2bNqlz587Kzs4u8766UkXW7c4779R7772nFStW6F//+pdSU1PVtm1bnThxwr6NgvVKm1t5qSzH2+eff64//vhDAwcOdJhfWY83yX21c0Xca/GYKyvOcaWvG+e4qz/eqtI5zhKv33W3y39DMcYUmnel8ZfPL802XTWmolRU3QoMHz5c27dv17fffuswv2/fvvb/37RpU7Vu3VpRUVFaunSp+vTpU8IelY+KqFu3bt3s/79Zs2Zq06aN6tatqwULFmj06NFO51aeKvp4mzt3rrp166bw8HCH+ZX9eJPcUztXxb3Wjrmy4BxX+rpxjrv6460qneO4cluCWrVqydPTs9BvRmlpaYV+IyoQGhpa5HgvLy8FBQWVOKZgm6WJW/Atz7LkVl4qqm6XGjFihL788kutWbNGN910U4n5hoWFKSoqSnv37r3ivrlTZahbgWrVqqlZs2b2mnC8lbzNgwcPatWqVXryySevmG9lOd4k99XOFXGvxWOuLDjHOVe3ApzjyqaqneNobkvg4+OjVq1aKTEx0WF+YmKi2rZtW+Q6bdq0KTR+5cqVat26tby9vUscU7DN0sSNjo5WaGiow5gLFy5o7dq1xeZWXiqqbtLF30yHDx+uJUuWaPXq1YqOjr5ividOnNDhw4cVFhZWqv1zl4qs2+Wys7OVkpJirwnH2/+NKWqb8+bNU3BwsHr06HHFfCvL8Sa5r3auiHstHnOlwTnOubpdjnNc2VS5c1x5fGutKit4/MbcuXPN7t27zahRo0y1atXMgQMHjDHGvPDCC+axxx6zjy94/Mazzz5rdu/ebebOnVvo8Rvfffed8fT0NJMmTTIpKSlm0qRJxT4KrLi4xlx8bEmNGjXMkiVLzI4dO8yf//znSvfYkvKu21NPPWVq1KhhvvnmG4fHkmRmZhpjjDlz5owZM2aMSUpKMvv37zdr1qwxbdq0MbVr176m6zZmzBjzzTffmF9//dVs3LjR9OzZ0wQGBnK8XaFuxhiTl5dnIiMjzfPPP18or8p+vBnjntplZ2ebrVu3mq1bt5qwsDDz3HPPma1bt5q9e/eWOq4x194xV5q6cY5zrm6c45yrmzFV8xxHc1sKb731lomKijI+Pj6mZcuWhR650qFDB4fx33zzjWnRooXx8fExderUMe+8806hbX788cfmlltuMd7e3qZhw4bm008/LVNcYy4+umT8+PEmNDTU+Pr6mvbt25sdO3a4ZqddoCLqJqnIz7x584wxxmRmZprY2Fhz4403Gm9vbxMZGWkGDBhgDh065PL9d1ZF1K3geY7e3t4mPDzc9OnTx+zatcthDMdb0f9OV6xYYSSZPXv2FFpWFY43Y1xfu/379xf57/Dy7XCOK3vdOMc5VzfOcc7/O62K5zibMf//DmMAAACgiuOeWwAAAFgGzS0AAAAsg+YWAAAAlkFzCwAAAMuguQUAAIBl0NwCAADAMmhuAQAAYBk0twAAALAMmlsAcAGbzabPP/+8otNwiwsXLqhevXr67rvvJEkHDhyQzWbTtm3bXBpn5syZuv/++126TQDXHppbACjGwIEDZbPZZLPZ5O3trZCQEMXExOjf//638vPzHcYeO3ZM3bp1K9V2q1ojPGfOHEVFRaldu3ZujTN48GBt2rRJ3377rVvjALA2mlsAKMG9996rY8eO6cCBA1q2bJk6deqkkSNHqmfPnsrNzbWPCw0Nla+vbwVm6j4zZszQk08+6fY4vr6+6tevn2bMmOH2WACsi+YWAErg6+ur0NBQ1a5dWy1bttSLL76oL774QsuWLdP8+fPt4y69GnvhwgUNHz5cYWFh8vPzU506dTRx4kRJUp06dSRJvXv3ls1ms0//8ssveuCBBxQSEqLrrrtOt99+u1atWuWQS506dZSQkKAnnnhCgYGBioyM1Jw5cxzG/Pbbb3r00UdVs2ZNVatWTa1bt9b3339vX/6f//xHrVq1kp+fn26++WZNmDDBoUm/3I8//qh9+/apR48exY7Jz8/X4MGD1aBBAx08eNBej9mzZ6tnz54KCAhQo0aNtGHDBu3bt08dO3ZUtWrV1KZNG/3yyy8O27r//vv1+eefKysrq9h4AFASmlsAKKPOnTurefPmWrJkSZHL33zzTX355Zf66KOPtGfPHi1cuNDexG7atEmSNG/ePB07dsw+ffbsWXXv3l2rVq3S1q1b1bVrV9133306dOiQw7Zff/11tW7dWlu3btWwYcP01FNP6aeffrJvo0OHDjp69Ki+/PJLJScna+zYsfZbKFasWKG4uDg988wz2r17t2bPnq358+frtddeK3Zf161bpwYNGqh69epFLr9w4YIeeeQRbd68Wd9++62ioqLsy/7xj3+of//+2rZtmxo2bKh+/fpp6NChGjdunDZv3ixJGj58uMP2WrdurZycHP3www/F5gQAJTIAgCINGDDAPPDAA0Uu69u3r2nUqJF9WpL57LPPjDHGjBgxwnTu3Nnk5+cXue6lY0vSuHFjM2PGDPt0VFSUiYuLs0/n5+eb4OBg88477xhjjJk9e7YJDAw0J06cKHJ7d999t0lISHCY9z//8z8mLCys2BxGjhxpOnfu7DBv//79RpJZv369ueeee0y7du3MH3/8UWgf/+u//ss+vWHDBiPJzJ071z5v8eLFxs/Pr1DMG264wcyfP7/YnACgJFy5BQAnGGNks9mKXDZw4EBt27ZNt9xyi5555hmtXLnyits7d+6cxo4dq8aNG+v666/Xddddp59++qnQldtbb73V/v9tNptCQ0OVlpYmSdq2bZtatGihmjVrFhljy5Yt+vvf/67rrrvO/hk8eLCOHTumzMzMItfJysqSn59fkcv+/Oc/6+zZs1q5cqVq1KhRaPmluYaEhEiSmjVr5jDv/PnzysjIcFjP39+/2HwA4EpobgHACSkpKYqOji5yWcuWLbV//3794x//UFZWlh555BE99NBDJW7vb3/7mz799FO99tprWr9+vbZt26ZmzZrpwoULDuO8vb0dpm02m/22A39//xJj5Ofna8KECdq2bZv9s2PHDu3du7fYBrZWrVo6depUkcu6d++u7du3a+PGjUUuvzTXgl8Eipp3+ZMnTp48qRtvvLHEfQGA4nhVdAIAUNWsXr1aO3bs0LPPPlvsmOrVq6tv377q27evHnroId177706efKkatasKW9vb+Xl5TmMX79+vQYOHKjevXtLunj/7IEDB8qU16233qp3333XHudyLVu21J49e1SvXr1Sb7NFixZ65513irxS/dRTT6lp06a6//77tXTpUnXo0KFM+Rbll19+0fnz59WiRYur3haAaxPNLQCUIDs7W6mpqcrLy9Px48e1fPlyTZw4UT179lT//v2LXOeNN95QWFiYbrvtNnl4eOjjjz9WaGiorr/+ekkXn3rw9ddfq127dvL19dUNN9ygevXqacmSJbrvvvtks9n08ssvF7qieSV//vOflZCQoF69emnixIkKCwvT1q1bFR4erjZt2uiVV15Rz549FRERoYcfflgeHh7avn27duzYoVdffbXIbXbq1Ennzp3Trl271LRp00LLR4wYoby8PPXs2VPLli3TXXfdVaacL7d+/XrdfPPNqlu37lVtB8C1i9sSAKAEy5cvV1hYmOrUqaN7771Xa9as0ZtvvqkvvvhCnp6eRa5z3XXXafLkyWrdurVuv/12HThwQF999ZU8PC6ecl9//XUlJiYqIiLCfoXyjTfe0A033KC2bdvqvvvuU9euXdWyZcsy5erj46OVK1cqODhY3bt3V7NmzTRp0iR7nl27dtX//u//KjExUbfffrv+9Kc/adq0aQ5POLhcUFCQ+vTpo/fff7/YMaNGjdKECRPUvXt3JSUllSnnyy1evFiDBw++qm0AuLbZjDGmopMAAFReO3bs0D333KN9+/YpMDDQbXF27typLl266Oeffy7yC2oAUBpcuQUAlKhZs2aaMmVKme8BLqujR4/qvffeo7EFcFW4cgsAAADL4MotAAAALIPmFgAAAJZBcwsAAADLoLkFAACAZdDcAgAAwDJobgEAAGAZNLcAAACwDJpbAAAAWAbNLQAAACzj/wH0xOAO+yOp2QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot heart rate distribution\n",
+    "if 'heart_rate' in df_cleaned.columns:\n",
+    "    plt.figure(figsize=(8, 4))\n",
+    "    plt.hist(df_cleaned['heart_rate'], bins=30, color='skyblue', edgecolor='black')\n",
+    "    plt.title(\"Heart Rate Distribution\")\n",
+    "    plt.xlabel(\"Heart Rate (bpm)\")\n",
+    "    plt.ylabel(\"Frequency\")\n",
+    "    plt.grid(True)\n",
+    "    plt.show()\n",
+    "\n",
+    "# Plot distance (km) if available\n",
+    "if 'distance_km' in df_cleaned.columns:\n",
+    "    plt.figure(figsize=(8, 4))\n",
+    "    plt.hist(df_cleaned['distance_km'], bins=20, color='lightgreen', edgecolor='black')\n",
+    "    plt.title(\"Distance Distribution (km)\")\n",
+    "    plt.xlabel(\"Distance (km)\")\n",
+    "    plt.ylabel(\"Frequency\")\n",
+    "    plt.grid(True)\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f458eeb5-f7a3-4ed7-a260-0dc2c3bfb0d7",
+   "metadata": {},
+   "source": [
+    "# Load – Saved the cleaned dataset to a new CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4581ef6c-925f-4cc9-87f5-f31aaaaac393",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Cleaned data saved to 'cleaned_garmin_run_data.csv'\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_cleaned.to_csv(\"cleaned_garmin_run_data.csv\", index=False)\n",
+    "print(\"✅ Cleaned data saved to 'cleaned_garmin_run_data.csv'\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98056b45-85a5-4848-9014-5854c922c2af",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}